Social contact, social cohesion, and social safety outcomes of children's school travel
Bibliographic record
Abstract
Much research on children's (school) travel focuses on explaining travel mode choice and how that relates to physical activity gained through active travel. However, another key component of children's lives and their travel relates to the social outcomes of travel such as social contact, social cohesion, and social safety. Children's independent and active travel has been shown to be positively related to social contacts with friends and neighbors which in turn is associated with social cohesion and feelings of safety in the neighborhood. However, the precise interrelationships between children's school travel and social aspects are not well understood. Using a Bayesian Belief Network approach, a data mining approach, it is possible to examine how (categorical) variables influence each other, both directly and indirectly. Using data from 601 primary school students and their parents in the Netherlands, a Bayesian Belief Network is estimated to examine the relationships between children's school travel characteristics (distance, mode, and travel party), and social domain factors (social contact with other children, social cohesion, and the perception of social safety). Along with those measures, variables are included related to child and household characteristics (age, gender, children's travel skills, household car ownership) and neighborhood characteristics (perceptions of traffic safety, socio-economic status of the neighborhoods, and population density). The results demonstrate that there is a clear relationship between the social domain factors, which are linked to how children travel. The findings suggest that shorter trips to school would increase social contact during travel, which improves social cohesion and, in turn improves the perception of social safety. Thus, not only do shorter distance trips increase the likelihood of active travel, which contributes to physical health, but they can also improve the social dimension of children's well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".